Robin Alexander Denhardt-Eriksson
Modelling and analysis of biological systems is crucial in order to quantitatively explain and predict their behaviour.
The importance of modelling in systems biology
becomes even more evident when tackling complex, large systems. Approaches
that rely on modelling can be used to tackle a range of problems, such as cellular
signalling or using genetically modified bacteria to synthesize human proteins
for medical purposes. These phenomena range across different spatiotemporal
scales, whether it is predicting the decay of red blood cells for blood transfusion
or finding engineering targets in order to increase the productivity of bioprocesses.
Reconciling models with experimental data and quantifying how uncertainty
propagates through these models is crucial to exploiting them to their full potential.
Uncertainty impacts not only the calibration and validation of a model, but
also the predictions extracted from it. Developing and implementing uncertainty
quantification methods into these models is a key step in exploring their behaviour
and accelerating the adoption of such methods in metabolic engineering.
In this thesis I firstly propose to implement and explore uncertainty quantification methods
on control coefficients obtained by Metabolic Control Analysis (MCA) of Genome Scale Metabolic Models (GEMs),
with the goal of quantifying uncertainty propagation from model parameters to control
coefficients. I additionally want to build Single Protein Models that describe
the effects of Post Translational Modifications (PTMs). The nature of the experimental data
available to calibrate these models makes uncertainty an inherent property, and therefore
Global Sensitivty Analysis (GSA) techniques are well suited to describing the links between
parameter uncertainty and model behaviour.
EPFL2021
